Segmentation of lung region from chest X-ray images using U-net

机译：使用U-net从胸部X射线图像中分割肺区域

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In recent years, many medical image analysis methods based on the Deep Learning techniques have been proposed. The DeepLearning techniques have been used for various medical applications such as organ segmentation and cancer detection. Segmentationof lung region from chest X-ray (CXR) images is also important task for computer-aided diagnosis (CAD). However, many methodsbased on Deep Learning techniques for this purpose were proposed, the regions where the lung and the heart overlap have beenexcluded from the target to be extracted in spite of the importance for detection of diseases. The aim of this paper is to extract wholelung regions from CRX images by using the U-net based method. As widely known, the U-net shows its high performance forvarious applications. As the result of the experiment, the authors archive 0.91 in the average of the Dice coefficient.

机译：近年来，已经提出了许多基于深度学习技术的医学图像分析方法。 Deep \ r \ nLearning技术已用于各种医疗应用，例如器官分割和癌症检测。从胸部X射线（CXR）图像分割肺区域也是计算机辅助诊断（CAD）的重要任务。然而，出于此目的，提出了许多基于深度学习技术的方法，尽管对于检测疾病很重要，但肺和心脏重叠的区域已从要提取的目标中排除了。本文的目的是通过基于U-net的方法从CRX图像中提取整个\ n \ n肺区域。众所周知，U-net在各种应用程序中均显示出高性能。作为实验的结果，作者将Dice系数的平均值存档为0.91。

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《International Forum on Medical Imaging in Asia 2019》|2019年|1105010.1-1105010.5|共5页
会议地点 0277-786X;1996-756X
作者
Keigo Furutani; Yasushi Hirano; Shoji Kido;
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Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi, Japan 755-8611;

Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi, Japan 755-8611;

Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1 Tokiwadai, Ube, Yamaguchi, Japan 755-8611;

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1. A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images [J] . Eri Matsuyama Journal of Biomedical Science and Engineering . 2021,第6期

机译：胸X射线图像中自动肺区分割的一种新方法
2. A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images [J] . Eri Matsuyama 生物医学工程（英文） . 2021,第006期

机译：A Novel Method for Automated Lung Region Segmentation in Chest X-Ray Images
3. Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures [J] . Ayat Abedalla, Malak Abdullah, Mahmoud Al-Ayyoub, PeerJ Computer Science . 2021,第a期

机译：使用U-NET使用有效网络和RESET架构的胸部X射线气胸分段
4. An Automatic Approach to Lung Region Segmentation in Chest X-Ray Images Using Adapted U-Net Architecture [C] . Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Conference on Medical Imaging: Physics of Medical Imaging . 2021

机译：使用U-NET架构的胸X射线图像中肺区分割的自动方法
5. High Throughput Image Labeling and Lung/Lobar Segmentation on Chest CT Using Deep Learning [D] . Wang, Xiaoyong. 2019

机译：深度吞吐图像标记和胸部CT使用深度学习的肺/洛洛段分割
6. Fully Automated Lung Lobe Segmentation in Volumetric Chest CT with 3D U-Net: Validation with Intra- and Extra-Datasets [O] . Jongha Park, Jihye Yun, Namkug Kim, 2020

机译：具有3D U-Net的容量胸部CT中的全自动肺瓣分段：使用内部数据集进行验证
7. Lung Region Segmentation in Chest X-Ray Images using Deep Convolutional Neural Networks [O] . R. D. S. Portela, J. R. G. Pereira, M. G. F. Costa, 2020

机译：使用深卷积神经网络胸部X射线图像中的肺区分割

Segmentation of lung region from chest X-ray images using U-net

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